When working with tabular data, such as data stored in spreadsheets or databases, Pandas is the right tool for you. Pandas will help you
to explore, clean and process your data. In Pandas, a data table is called a DataFrame.

Pandas supports the integration with many file formats or data sources out of the box (csv, excel, sql, json, parquet,…). Importing data from each of these
data sources is provided by function with the prefix read_*. Similarly, the to_* methods are used to store data.

There is no need to loop over all rows of your data table to do calculations. Data manipulations on a column work elementwise.
Adding a column to a DataFrame based on existing data in other columns is straightforward.

Basic statistics (mean, median, min, max, counts…) are easily calculable. These or custom aggregations can be applied on the entire
data set, a sliding window of the data or grouped by categories. The latter is also known as the split-apply-combine approach.

Change the structure of your data table in multiple ways. You can melt() your data table from wide to long/tidy form or pivot()
from long to wide format. With aggregations built-in, a pivot table is created with a sinlge command.

Currently working with other software for data manipulation in a tabular format? You’re probably familiar to typical
data operations and know what to do with your tabular data, but lacking the syntax to execute these operations. Get to know
the pandas syntax by looking for equivalents from the software you already know:

The R programming language provides the data.frame data structure and multiple packages,
such as tidyverse use and extend data.frames for convenient data handling
functionalities similar to pandas.

The SAS statistical software suite
also provides the data set corresponding to the pandas data.frame.
Also vectorized operations, filtering, string processing operations,... from SAS have similar
functions in pandas.